{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e6340380",
   "metadata": {},
   "source": [
    "# Databases - Spatial"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff422ccf",
   "metadata": {},
   "source": [
    "This tutorial covers the spatial databases of `plans`. Unlike `SemiSpatialDB`, they keep the geometries inside the database file, a GeoPackage, so records and places travel together and open in any GIS. There is one class per kind of geometry, each with its own layer and record key:\n",
    "\n",
    "| Class | Layer | Geometry | Record key |\n",
    "|---|---|---|---|\n",
    "| `ZonesDB` | `zones` | polygons | `zone_id` |\n",
    "| `PathsDB` | `paths` | lines | `path_id` |\n",
    "| `SitesDB` | `sites` | points | `site_id` |\n",
    "| `SpatialDB` | `features` | any, mixed | `feature_id` |\n",
    "\n",
    "The examples are a rainfall grid, a river network and a few weather stations.\n",
    "\n",
    "```{seealso}\n",
    "Check out the {doc}`Databases - Station networks <databases_02_semispatial>` tutorial first.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c8d1413b",
   "metadata": {},
   "source": [
    "## Notebook setup\n",
    "\n",
    "For users running this tutorial as a Jupyter Notebook, this cell must be executed first:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "02e734b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:36.853308Z",
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     "shell.execute_reply": "2026-11-09T12:45:37.574753Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Outputs will be saved to: ./outputs\\databases\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import json\n",
    "from pathlib import Path\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import LineString, Point, box\n",
    "\n",
    "# Install `plans` in `google.colab`.\n",
    "# Use `pip install plans` for other environments.\n",
    "\n",
    "if \"google.colab\" in sys.modules:\n",
    "    import os\n",
    "    os.system(f\"{sys.executable} -m pip install -q plans\")\n",
    "\n",
    "from plans.databases import ZonesDB, PathsDB, SitesDB, SpatialDB\n",
    "from plans.datasets.analyst import GeoUnivar\n",
    "\n",
    "# This avoids warnings related to uninstalled fonts\n",
    "import logging\n",
    "# Set the matplotlib font manager logger to only show errors (hides warnings)\n",
    "logging.getLogger('matplotlib.font_manager').setLevel(logging.ERROR)\n",
    "\n",
    "# define output folder\n",
    "OUTPUT_DIR = Path(\"outputs/databases\")\n",
    "OUTPUT_DIR.mkdir(parents=True, exist_ok=True)\n",
    "print(f\"Outputs will be saved to: ./{OUTPUT_DIR}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d73a32",
   "metadata": {},
   "source": [
    "All examples use monthly data from 2016 to 2020, and the same helper to write a specs file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8fa840e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:37.577052Z",
     "iopub.status.busy": "2026-11-09T12:45:37.576791Z",
     "iopub.status.idle": "2026-11-09T12:45:37.581446Z",
     "shell.execute_reply": "2026-11-09T12:45:37.580983Z"
    }
   },
   "outputs": [],
   "source": [
    "rng = np.random.default_rng(seed=3)  # ensure reproducibility\n",
    "dates = pd.date_range(start=\"2016-01-01\", end=\"2020-12-01\", freq=\"MS\")\n",
    "season = np.cos(2 * np.pi * (dates.month - 1) / 12)\n",
    "\n",
    "\n",
    "def write_specs(file_specs, specs):\n",
    "    # each spec is (name, abstract, variable, statistic, source, extent)\n",
    "    rows = [\n",
    "        {\n",
    "            \"name\": name,\n",
    "            \"abstract\": abstract,\n",
    "            \"method\": \"synthetic\",\n",
    "            \"timestep\": \"1M\",\n",
    "            \"extent\": extent,\n",
    "            \"scale\": 1,\n",
    "            \"offset\": 0,\n",
    "            \"start\": \"2016-01-01\",\n",
    "            \"end\": None,\n",
    "            \"variable_name\": variable,\n",
    "            \"statistic_name\": statistic,\n",
    "            \"source_name\": source,\n",
    "        }\n",
    "        for name, abstract, variable, statistic, source, extent in specs\n",
    "    ]\n",
    "    file_specs.write_text(json.dumps(rows, indent=2), encoding=\"utf-8\")\n",
    "    return file_specs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e6ffd027",
   "metadata": {},
   "source": [
    "## Zones: a rainfall grid\n",
    "\n",
    "Gridded products, such as satellite rainfall, are polygons in a regular grid. Lets make an 8 by 6 grid of half-degree cells, each with a unique `name`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "49450f6d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:37.582990Z",
     "iopub.status.busy": "2026-11-09T12:45:37.582835Z",
     "iopub.status.idle": "2026-11-09T12:45:37.643239Z",
     "shell.execute_reply": "2026-11-09T12:45:37.642787Z"
    }
   },
   "outputs": [
    {
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>R1C1</td>\n",
       "      <td>POLYGON ((-53.5 -29.5, -53.5 -29, -54 -29, -54...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>R1C2</td>\n",
       "      <td>POLYGON ((-53 -29.5, -53 -29, -53.5 -29, -53.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>R1C3</td>\n",
       "      <td>POLYGON ((-52.5 -29.5, -52.5 -29, -53 -29, -53...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>R1C4</td>\n",
       "      <td>POLYGON ((-52 -29.5, -52 -29, -52.5 -29, -52.5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>R1C5</td>\n",
       "      <td>POLYGON ((-51.5 -29.5, -51.5 -29, -52 -29, -52...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name                                           geometry\n",
       "0  R1C1  POLYGON ((-53.5 -29.5, -53.5 -29, -54 -29, -54...\n",
       "1  R1C2  POLYGON ((-53 -29.5, -53 -29, -53.5 -29, -53.5...\n",
       "2  R1C3  POLYGON ((-52.5 -29.5, -52.5 -29, -53 -29, -53...\n",
       "3  R1C4  POLYGON ((-52 -29.5, -52 -29, -52.5 -29, -52.5...\n",
       "4  R1C5  POLYGON ((-51.5 -29.5, -51.5 -29, -52 -29, -52..."
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cells = []\n",
    "for row in range(6):\n",
    "    for col in range(8):\n",
    "        x0, y0 = -54.0 + 0.5 * col, -29.0 - 0.5 * (row + 1)\n",
    "        cells.append({\"name\": f\"R{row + 1}C{col + 1}\", \"geometry\": box(x0, y0, x0 + 0.5, y0 + 0.5)})\n",
    "grid = gpd.GeoDataFrame(data=cells, crs=\"EPSG:4674\")\n",
    "\n",
    "file_grid = OUTPUT_DIR / \"grid.gpkg\"\n",
    "grid.to_file(filename=file_grid, layer=\"grid\", driver=\"GPKG\")\n",
    "grid.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e8a285cf",
   "metadata": {},
   "source": [
    "A spatial database is created from the usual setup plus a `[spatial]` section, with the source `file` of the geometries and, if the file has several, the `layer`. The database must be a `.gpkg` file. Each zone gets a generated `id`, which is only stable inside this file, so the `name` is the reference to use elsewhere:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bc1b4355",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:37.644690Z",
     "iopub.status.busy": "2026-11-09T12:45:37.644533Z",
     "iopub.status.idle": "2026-11-09T12:45:37.754031Z",
     "shell.execute_reply": "2026-11-09T12:45:37.753449Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[Grid]\n",
      "  records  (0 rows)\n",
      "    id                   INTEGER\n",
      "    inserted             INTEGER\n",
      "    datetime             INTEGER\n",
      "    value                REAL\n",
      "    flag_value           INTEGER\n",
      "    spec_id              INTEGER\n",
      "    zone_id              INTEGER\n",
      "\n"
     ]
    }
   ],
   "source": [
    "file_specs = write_specs(\n",
    "    file_specs=OUTPUT_DIR / \"grid_specs.json\",\n",
    "    specs=[(\"PPT_MONTHLY_GRID\", \"Monthly rainfall per grid cell\", \"precipitation\", \"sum\", \"CHIRPS v2.0\", \"cell\")],\n",
    ")\n",
    "zdb = ZonesDB(name=\"Grid\")\n",
    "zdb.new(\n",
    "    config={\n",
    "        \"database\": {\"path\": str(OUTPUT_DIR / \"grid_db.gpkg\")},\n",
    "        \"specs\": {\"path\": str(file_specs)},\n",
    "        \"spatial\": {\"file\": str(file_grid), \"layer\": \"grid\"},\n",
    "    },\n",
    "    overwrite=True,\n",
    ")\n",
    "print(zdb.inspect_schema(table=\"records\", quiet=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d460561a",
   "metadata": {},
   "source": [
    "Rainfall grows to the east. Records can name their zone with `zone_name` instead of `zone_id`; the database resolves it, and refuses names it does not know:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2974d049",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2026-11-09T12:45:37.809567Z"
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   "outputs": [
    {
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>value</th>\n",
       "      <th>zone_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00+00:00</td>\n",
       "      <td>133.506982</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-01 00:00:00+00:00</td>\n",
       "      <td>78.772256</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-01-01 00:00:00+00:00</td>\n",
       "      <td>53.506193</td>\n",
       "      <td>8</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-02-01 00:00:00+00:00</td>\n",
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      ],
      "text/plain": [
       "                   datetime       value  zone_id\n",
       "0 2016-01-01 00:00:00+00:00  133.506982        1\n",
       "1 2016-02-01 00:00:00+00:00   78.772256        1\n",
       "2 2016-01-01 00:00:00+00:00   53.506193        8\n",
       "3 2016-02-01 00:00:00+00:00   44.953056        8"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = []\n",
    "for cell in grid.itertuples():\n",
    "    east = cell.geometry.centroid.x + 54.0  # 0 to 4 degrees\n",
    "    mean_ppt = (90 + 20 * east) * (1 - 0.3 * season)\n",
    "    frames.append(pd.DataFrame({\"datetime\": dates, \"value\": rng.gamma(shape=6, scale=mean_ppt / 6), \"zone_name\": cell.name}))\n",
    "df = pd.concat(frames, ignore_index=True)\n",
    "\n",
    "zdb.insert_records(dataframe=df, spec_name=\"PPT_MONTHLY_GRID\", flag=1)\n",
    "zdb.query_records(query_dict={\"zone_name\": [\"R1C1\", \"R1C8\"], \"start\": \"2016-01-01\", \"end\": \"2016-02-01\"})[[\"datetime\", \"value\", \"zone_id\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8f596135",
   "metadata": {},
   "source": [
    "The geometries come back with `.get_features()`, with `id` as the first column. It matches `zone_id` in the records, so a mean per zone joins straight to its cell:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cdc19658",
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     "shell.execute_reply": "2026-11-09T12:45:38.292211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1003.94x425.197 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = zdb.query_records(query_dict={\"spec_name\": \"PPT_MONTHLY_GRID\"})\n",
    "mean_monthly = df.groupby(\"zone_id\")[\"value\"].mean().rename(\"ppt_mean\")\n",
    "zones = zdb.get_features().merge(mean_monthly, left_on=\"id\", right_index=True)\n",
    "\n",
    "gv = GeoUnivar(name=\"Grid rainfall\", alias=\"PPT\")\n",
    "gv.varname = \"Mean monthly rainfall\"\n",
    "gv.units = \"mm\"\n",
    "gv.set_data(input_df=zones, input_varfield=\"ppt_mean\")\n",
    "gv.view()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "8686573c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:38.294293Z",
     "iopub.status.busy": "2026-11-09T12:45:38.294121Z",
     "iopub.status.idle": "2026-11-09T12:45:38.296832Z",
     "shell.execute_reply": "2026-11-09T12:45:38.296419Z"
    }
   },
   "outputs": [],
   "source": [
    "zdb.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f3aa2e1e",
   "metadata": {},
   "source": [
    "## Paths: a river network\n",
    "\n",
    "Lines hold data that flows along them, such as streamflow in a river. Lets build a small network: a main river in four reaches, from west to east, and three tributaries. Each reach has the drainage area at its downstream end:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8cec1cfd",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:38.298287Z",
     "iopub.status.busy": "2026-11-09T12:45:38.298124Z",
     "iopub.status.idle": "2026-11-09T12:45:38.336307Z",
     "shell.execute_reply": "2026-11-09T12:45:38.335697Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>area_km2</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M1</td>\n",
       "      <td>800</td>\n",
       "      <td>LINESTRING (-53.5 -30, -52.8 -30.1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M2</td>\n",
       "      <td>1900</td>\n",
       "      <td>LINESTRING (-52.8 -30.1, -52 -30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M3</td>\n",
       "      <td>3200</td>\n",
       "      <td>LINESTRING (-52 -30, -51.2 -30.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M4</td>\n",
       "      <td>4800</td>\n",
       "      <td>LINESTRING (-51.2 -30.2, -50.4 -30.1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>T1</td>\n",
       "      <td>500</td>\n",
       "      <td>LINESTRING (-52.9 -29.2, -52.8 -30.1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>T2</td>\n",
       "      <td>700</td>\n",
       "      <td>LINESTRING (-52.1 -30.9, -52 -30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>T3</td>\n",
       "      <td>900</td>\n",
       "      <td>LINESTRING (-51 -29.3, -51.2 -30.2)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  name  area_km2                               geometry\n",
       "0   M1       800    LINESTRING (-53.5 -30, -52.8 -30.1)\n",
       "1   M2      1900      LINESTRING (-52.8 -30.1, -52 -30)\n",
       "2   M3      3200      LINESTRING (-52 -30, -51.2 -30.2)\n",
       "3   M4      4800  LINESTRING (-51.2 -30.2, -50.4 -30.1)\n",
       "4   T1       500  LINESTRING (-52.9 -29.2, -52.8 -30.1)\n",
       "5   T2       700      LINESTRING (-52.1 -30.9, -52 -30)\n",
       "6   T3       900    LINESTRING (-51 -29.3, -51.2 -30.2)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nodes = {\n",
    "    \"a\": (-53.5, -30.0), \"b\": (-52.8, -30.1), \"c\": (-52.0, -30.0), \"d\": (-51.2, -30.2), \"outlet\": (-50.4, -30.1),\n",
    "    \"t1\": (-52.9, -29.2), \"t2\": (-52.1, -30.9), \"t3\": (-51.0, -29.3),\n",
    "}\n",
    "reaches = [\n",
    "    (\"M1\", \"a\", \"b\", 800), (\"M2\", \"b\", \"c\", 1900), (\"M3\", \"c\", \"d\", 3200), (\"M4\", \"d\", \"outlet\", 4800),\n",
    "    (\"T1\", \"t1\", \"b\", 500), (\"T2\", \"t2\", \"c\", 700), (\"T3\", \"t3\", \"d\", 900),\n",
    "]\n",
    "rivers = gpd.GeoDataFrame(\n",
    "    data={\n",
    "        \"name\": [r[0] for r in reaches],\n",
    "        \"area_km2\": [r[3] for r in reaches],\n",
    "        \"geometry\": [LineString([nodes[r[1]], nodes[r[2]]]) for r in reaches],\n",
    "    },\n",
    "    crs=\"EPSG:4674\",\n",
    ")\n",
    "file_rivers = OUTPUT_DIR / \"rivers.gpkg\"\n",
    "rivers.to_file(filename=file_rivers, driver=\"GPKG\")\n",
    "rivers"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f40f10f",
   "metadata": {},
   "source": [
    "Only the `name` and the geometry enter the layer by default. Any other column must be declared in a `columns_map`, as `{layer column: source column}`, so the drainage area is declared in the setup below.\n",
    "\n",
    "Monthly streamflow is the drainage area times a runoff depth, converted to cubic meters per second. Runoff shares one noise for the whole network, so wet months are wet everywhere:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "441565fe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:38.338439Z",
     "iopub.status.busy": "2026-11-09T12:45:38.338254Z",
     "iopub.status.idle": "2026-11-09T12:45:38.458339Z",
     "shell.execute_reply": "2026-11-09T12:45:38.457882Z"
    }
   },
   "outputs": [],
   "source": [
    "file_specs = write_specs(\n",
    "    file_specs=OUTPUT_DIR / \"rivers_specs.json\",\n",
    "    specs=[(\"Q_MONTHLY\", \"Monthly mean streamflow per reach\", \"standard streamflow\", \"mean\", \"ANA\", \"reach\")],\n",
    ")\n",
    "pdb = PathsDB(name=\"Rivers\")\n",
    "pdb.new(\n",
    "    config={\n",
    "        \"database\": {\"path\": str(OUTPUT_DIR / \"rivers_db.gpkg\")},\n",
    "        \"specs\": {\"path\": str(file_specs)},\n",
    "        \"spatial\": {\"file\": str(file_rivers), \"columns_map\": {\"area_km2\": \"area_km2\"}},\n",
    "    },\n",
    "    overwrite=True,\n",
    ")\n",
    "\n",
    "runoff_mm = 40 * (1 - 0.4 * season) * rng.gamma(shape=5, scale=0.2, size=len(dates))\n",
    "seconds = dates.days_in_month * 86400\n",
    "frames = []\n",
    "for reach in rivers.itertuples():\n",
    "    flow = reach.area_km2 * 1e6 * runoff_mm / 1000 / seconds\n",
    "    frames.append(pd.DataFrame({\"datetime\": dates, \"value\": flow, \"path_name\": reach.name}))\n",
    "pdb.insert_records(dataframe=pd.concat(frames, ignore_index=True), spec_name=\"Q_MONTHLY\", flag=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dec08332",
   "metadata": {},
   "source": [
    "Lines go through `GeoUnivar` too. For lines, the edge color is the line color, so the `edgecolor_map` key must be `None` to let the color map paint them. Flow grows downstream:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "43a6c359",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:38.460691Z",
     "iopub.status.busy": "2026-11-09T12:45:38.460238Z",
     "iopub.status.idle": "2026-11-09T12:45:38.860051Z",
     "shell.execute_reply": "2026-11-09T12:45:38.859531Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1003.94x425.197 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pdb.query_records(query_dict={\"spec_name\": \"Q_MONTHLY\"})\n",
    "mean_flow = df.groupby(\"path_id\")[\"value\"].mean().rename(\"q_mean\")\n",
    "paths = pdb.get_features().merge(mean_flow, left_on=\"id\", right_index=True)\n",
    "\n",
    "gv = GeoUnivar(name=\"River flow\", alias=\"Q\")\n",
    "gv.varname = \"Mean streamflow\"\n",
    "gv.units = \"m3/s\"\n",
    "gv.set_data(input_df=paths, input_varfield=\"q_mean\")\n",
    "gv.view_specs[\"edgecolor_map\"] = None\n",
    "gv.view()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e264d08b",
   "metadata": {},
   "source": [
    "In wide format, the reaches of the main river show the flow building up along the way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d09fcb4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:38.862114Z",
     "iopub.status.busy": "2026-11-09T12:45:38.861899Z",
     "iopub.status.idle": "2026-11-09T12:45:39.002100Z",
     "shell.execute_reply": "2026-11-09T12:45:39.001568Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x450 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "wide = pdb.query_records(query_dict={\"path_name\": [\"M1\", \"M2\", \"M3\", \"M4\"], \"wide\": True})\n",
    "names = pdb.get_features().set_index(\"id\")[\"name\"]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(8, 3))\n",
    "for (spec, path_id), series in wide.items():\n",
    "    ax.plot(series.index, series.values, lw=1, label=names[path_id])\n",
    "ax.set_ylabel(\"Streamflow (m3/s)\")\n",
    "ax.legend(ncols=4, frameon=False)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "03b6cf46",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:39.003714Z",
     "iopub.status.busy": "2026-11-09T12:45:39.003556Z",
     "iopub.status.idle": "2026-11-09T12:45:39.006202Z",
     "shell.execute_reply": "2026-11-09T12:45:39.005822Z"
    }
   },
   "outputs": [],
   "source": [
    "pdb.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "58443673",
   "metadata": {},
   "source": [
    "## Sites: weather stations\n",
    "\n",
    "Points are sites, such as weather stations. This source is a shapefile, and its key is not called `name`, but `station`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "af7b45c8",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:39.007623Z",
     "iopub.status.busy": "2026-11-09T12:45:39.007478Z",
     "iopub.status.idle": "2026-11-09T12:45:39.037060Z",
     "shell.execute_reply": "2026-11-09T12:45:39.036458Z"
    }
   },
   "outputs": [],
   "source": [
    "stations = gpd.GeoDataFrame(\n",
    "    data={\"station\": [\"Coast\", \"Valley\", \"Plateau\"], \"operator\": [\"INMET\", \"INMET\", \"ANA\"]},\n",
    "    geometry=gpd.points_from_xy(x=[-50.5, -52.0, -53.2], y=[-30.2, -30.0, -28.9]),\n",
    "    crs=\"EPSG:4674\",\n",
    ")\n",
    "file_stations = OUTPUT_DIR / \"sites.shp\"\n",
    "stations.to_file(filename=file_stations)\n",
    "\n",
    "file_specs = write_specs(\n",
    "    file_specs=OUTPUT_DIR / \"sites_specs.json\",\n",
    "    specs=[(\"TAS_MONTHLY\", \"Monthly mean air temperature\", \"surface air temperature\", \"mean\", \"INMET\", \"station\")],\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92bac487",
   "metadata": {},
   "source": [
    "The setup so far was a dict, but it can also be a TOML or JSON file with the same structure. The `spatial` section takes the source `file`, and the `columns_map` declares `{layer column: source column}`. The `name` entry sets the unique key of the sites, from text or integer values, stored as text. A source column may be used more than once, and columns left out of the map stay out of the layer. A shapefile holds a single layer, so there is no `layer` key:\n",
    "\n",
    "`````{tab-set}\n",
    ":sync-group: setup\n",
    "\n",
    "````{tab-item} TOML\n",
    ":sync: toml\n",
    "\n",
    "```toml\n",
    "[database]\n",
    "path = \"outputs/databases/sites_db.gpkg\"\n",
    "\n",
    "[specs]\n",
    "path = \"outputs/databases/sites_specs.json\"\n",
    "\n",
    "[spatial]\n",
    "file = \"outputs/databases/sites.shp\"\n",
    "\n",
    "[spatial.columns_map]\n",
    "name = \"station\"\n",
    "operator = \"operator\"\n",
    "```\n",
    "````\n",
    "\n",
    "````{tab-item} JSON\n",
    ":sync: json\n",
    "\n",
    "```json\n",
    "{\n",
    "  \"database\": {\"path\": \"outputs/databases/sites_db.gpkg\"},\n",
    "  \"specs\": {\"path\": \"outputs/databases/sites_specs.json\"},\n",
    "  \"spatial\": {\n",
    "    \"file\": \"outputs/databases/sites.shp\",\n",
    "    \"columns_map\": {\n",
    "      \"name\": \"station\",\n",
    "      \"operator\": \"operator\"\n",
    "    }\n",
    "  }\n",
    "}\n",
    "```\n",
    "````\n",
    "`````\n",
    "\n",
    "Lets write the TOML version, and pass its path to `.new()`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f7bdfd3d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:39.038934Z",
     "iopub.status.busy": "2026-11-09T12:45:39.038773Z",
     "iopub.status.idle": "2026-11-09T12:45:39.191584Z",
     "shell.execute_reply": "2026-11-09T12:45:39.191092Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>operator</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Coast</td>\n",
       "      <td>INMET</td>\n",
       "      <td>POINT (-50.5 -30.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Valley</td>\n",
       "      <td>INMET</td>\n",
       "      <td>POINT (-52 -30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Plateau</td>\n",
       "      <td>ANA</td>\n",
       "      <td>POINT (-53.2 -28.9)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id     name operator             geometry\n",
       "0   1    Coast    INMET  POINT (-50.5 -30.2)\n",
       "1   2   Valley    INMET      POINT (-52 -30)\n",
       "2   3  Plateau      ANA  POINT (-53.2 -28.9)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "file_setup = OUTPUT_DIR / \"sites_setup.toml\"\n",
    "file_setup.write_text(\n",
    "    f\"\"\"[database]\n",
    "path = \"{(OUTPUT_DIR / 'sites_db.gpkg').as_posix()}\"\n",
    "\n",
    "[specs]\n",
    "path = \"{file_specs.as_posix()}\"\n",
    "\n",
    "[spatial]\n",
    "file = \"{file_stations.as_posix()}\"\n",
    "\n",
    "[spatial.columns_map]\n",
    "name = \"station\"\n",
    "operator = \"operator\"\n",
    "\"\"\",\n",
    "    encoding=\"utf-8\",\n",
    ")\n",
    "sdb = SitesDB(name=\"Stations\")\n",
    "sdb.new(config=file_setup, overwrite=True)\n",
    "sdb.get_features()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "138976b4",
   "metadata": {},
   "source": [
    "The source format only changes the `spatial` section. A GeoPackage may hold several layers, so the setup names one with `layer`; without it, the first layer is read, with only a warning. A shapefile has two traps of its own. Its field names stop at 10 characters and the map must use them as read, so a field written as `station_name` is mapped as `station_na`. Its CRS lives in the `.prj` file next to it, and without that file the layer has no CRS.\n",
    "\n",
    "`````{tab-set}\n",
    ":sync-group: setup\n",
    "\n",
    "````{tab-item} TOML\n",
    ":sync: toml\n",
    "\n",
    "```toml\n",
    "[spatial]\n",
    "file = \"network.gpkg\"\n",
    "layer = \"stations\"\n",
    "\n",
    "[spatial.columns_map]\n",
    "name = \"station_name\"\n",
    "```\n",
    "````\n",
    "\n",
    "````{tab-item} JSON\n",
    ":sync: json\n",
    "\n",
    "```json\n",
    "{\n",
    "  \"spatial\": {\n",
    "    \"file\": \"network.gpkg\",\n",
    "    \"layer\": \"stations\",\n",
    "    \"columns_map\": {\"name\": \"station_name\"}\n",
    "  }\n",
    "}\n",
    "```\n",
    "````\n",
    "`````\n",
    "\n",
    "The layer only accepts points; any other geometry is refused:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "5943a84c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:39.193371Z",
     "iopub.status.busy": "2026-11-09T12:45:39.193198Z",
     "iopub.status.idle": "2026-11-09T12:45:39.199634Z",
     "shell.execute_reply": "2026-11-09T12:45:39.199200Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Geometry type(s) ['Polygon'] not accepted in sites. Valid: ['Point']\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    sdb.insert_features(gdf=grid.head(1)[[\"name\", \"geometry\"]])\n",
    "except ValueError as error:\n",
    "    print(error)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50accd5b",
   "metadata": {},
   "source": [
    "Networks grow. The `.insert_features()` method adds sites later. Here, a new station comes in UTM coordinates; it is reprojected to the CRS of the layer, and gets the next id. It takes a `columns_map` as well; layer columns left out stay empty, and columns the layer does not have are refused:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "280f6b6c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:39.201797Z",
     "iopub.status.busy": "2026-11-09T12:45:39.201520Z",
     "iopub.status.idle": "2026-11-09T12:45:39.237807Z",
     "shell.execute_reply": "2026-11-09T12:45:39.237307Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>operator</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Coast</td>\n",
       "      <td>INMET</td>\n",
       "      <td>POINT (-50.5 -30.2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Valley</td>\n",
       "      <td>INMET</td>\n",
       "      <td>POINT (-52 -30)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Plateau</td>\n",
       "      <td>ANA</td>\n",
       "      <td>POINT (-53.2 -28.9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Ridge</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POINT (-51.5166 -29.64896)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id     name operator                    geometry\n",
       "0   1    Coast    INMET         POINT (-50.5 -30.2)\n",
       "1   2   Valley    INMET             POINT (-52 -30)\n",
       "2   3  Plateau      ANA         POINT (-53.2 -28.9)\n",
       "3   4    Ridge      NaN  POINT (-51.5166 -29.64896)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_station = gpd.GeoDataFrame(\n",
    "    data={\"name\": [\"Ridge\"]},\n",
    "    geometry=gpd.points_from_xy(x=[450000], y=[6720000]),\n",
    "    crs=\"EPSG:31982\",  # SIRGAS 2000 / UTM zone 22S\n",
    ")\n",
    "sdb.insert_features(gdf=new_station)\n",
    "sdb.get_features()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0014cc8",
   "metadata": {},
   "source": [
    "Records of all four stations, named by `site_name`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4646c79",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:39.239454Z",
     "iopub.status.busy": "2026-11-09T12:45:39.239286Z",
     "iopub.status.idle": "2026-11-09T12:45:39.269609Z",
     "shell.execute_reply": "2026-11-09T12:45:39.269088Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>name</th>\n",
       "      <th>Coast</th>\n",
       "      <th>Valley</th>\n",
       "      <th>Plateau</th>\n",
       "      <th>Ridge</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datetime</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-01 00:00:00+00:00</th>\n",
       "      <td>24.976864</td>\n",
       "      <td>23.332622</td>\n",
       "      <td>18.968884</td>\n",
       "      <td>17.892839</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01 00:00:00+00:00</th>\n",
       "      <td>23.473974</td>\n",
       "      <td>24.967774</td>\n",
       "      <td>17.426581</td>\n",
       "      <td>16.786563</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01 00:00:00+00:00</th>\n",
       "      <td>23.183442</td>\n",
       "      <td>21.683340</td>\n",
       "      <td>17.036439</td>\n",
       "      <td>13.836584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01 00:00:00+00:00</th>\n",
       "      <td>19.535946</td>\n",
       "      <td>19.460948</td>\n",
       "      <td>15.170996</td>\n",
       "      <td>13.308316</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-01 00:00:00+00:00</th>\n",
       "      <td>16.673713</td>\n",
       "      <td>17.376706</td>\n",
       "      <td>11.503583</td>\n",
       "      <td>10.844654</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "name                           Coast     Valley    Plateau      Ridge\n",
       "datetime                                                             \n",
       "2016-01-01 00:00:00+00:00  24.976864  23.332622  18.968884  17.892839\n",
       "2016-02-01 00:00:00+00:00  23.473974  24.967774  17.426581  16.786563\n",
       "2016-03-01 00:00:00+00:00  23.183442  21.683340  17.036439  13.836584\n",
       "2016-04-01 00:00:00+00:00  19.535946  19.460948  15.170996  13.308316\n",
       "2016-05-01 00:00:00+00:00  16.673713  17.376706  11.503583  10.844654"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frames = []\n",
    "for name, base in {\"Coast\": 20, \"Valley\": 19, \"Plateau\": 15, \"Ridge\": 13}.items():\n",
    "    tas = base + 5 * season + rng.normal(scale=0.8, size=len(dates))\n",
    "    frames.append(pd.DataFrame({\"datetime\": dates, \"value\": tas, \"site_name\": name}))\n",
    "sdb.insert_records(dataframe=pd.concat(frames, ignore_index=True), spec_name=\"TAS_MONTHLY\", flag=1)\n",
    "\n",
    "wide = sdb.query_records(query_dict={\"wide\": True})\n",
    "wide.columns = sdb.get_features().set_index(\"id\").loc[wide.columns.get_level_values(\"site_id\"), \"name\"]\n",
    "wide.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "af7f1932",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:39.271249Z",
     "iopub.status.busy": "2026-11-09T12:45:39.271072Z",
     "iopub.status.idle": "2026-11-09T12:45:39.274188Z",
     "shell.execute_reply": "2026-11-09T12:45:39.273751Z"
    }
   },
   "outputs": [],
   "source": [
    "sdb.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7582dd2",
   "metadata": {},
   "source": [
    "```{note}\n",
    "`FieldDB` is a `SitesDB` meant for in situ measurements, such as a climate station network. It works the same way for now. For samples analysed in a laboratory, see the {doc}`Databases - Laboratory <databases_04_lab>` tutorial. For river reaches with their catchments, see the {doc}`Databases - Hydrology <databases_05_hydrology>` tutorial. For dense series such as hourly stations, see the {doc}`Databases - Dense series <databases_06_dense>` tutorial.\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2cd195f0",
   "metadata": {},
   "source": [
    "## Any geometry: `SpatialDB`\n",
    "\n",
    "When places of different kinds share one database, `SpatialDB` takes any geometry in a single `features` layer. Here, a catchment, its outlet and its main river:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "60fae6ba",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:39.276270Z",
     "iopub.status.busy": "2026-11-09T12:45:39.275986Z",
     "iopub.status.idle": "2026-11-09T12:45:39.389351Z",
     "shell.execute_reply": "2026-11-09T12:45:39.388882Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Catchment</td>\n",
       "      <td>Polygon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Outlet</td>\n",
       "      <td>Point</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Main river</td>\n",
       "      <td>LineString</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id        name geometry_type\n",
       "0   1   Catchment       Polygon\n",
       "1   2      Outlet         Point\n",
       "2   3  Main river    LineString"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "features = gpd.GeoDataFrame(\n",
    "    data={\"name\": [\"Catchment\", \"Outlet\", \"Main river\"]},\n",
    "    geometry=[\n",
    "        box(-53.6, -31.0, -50.3, -29.1),\n",
    "        Point(nodes[\"outlet\"]),\n",
    "        LineString([nodes[k] for k in (\"a\", \"b\", \"c\", \"d\", \"outlet\")]),\n",
    "    ],\n",
    "    crs=\"EPSG:4674\",\n",
    ")\n",
    "file_features = OUTPUT_DIR / \"features.gpkg\"\n",
    "features.to_file(filename=file_features, driver=\"GPKG\")\n",
    "\n",
    "file_specs = write_specs(\n",
    "    file_specs=OUTPUT_DIR / \"features_specs.json\",\n",
    "    specs=[(\"PPT_MONTHLY_AREAL\", \"Monthly areal rainfall\", \"precipitation\", \"sum\", \"CHIRPS v2.0\", \"catchment\")],\n",
    ")\n",
    "fdb = SpatialDB(name=\"Catchment\")\n",
    "fdb.new(\n",
    "    config={\n",
    "        \"database\": {\"path\": str(OUTPUT_DIR / \"features_db.gpkg\")},\n",
    "        \"specs\": {\"path\": str(file_specs)},\n",
    "        \"spatial\": {\"file\": str(file_features)},\n",
    "    },\n",
    "    overwrite=True,\n",
    ")\n",
    "fdb.get_features()[[\"id\", \"name\"]].assign(geometry_type=fdb.get_features().geom_type)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ed6260d5",
   "metadata": {},
   "source": [
    "Records point to a feature by `feature_id` or `feature_name`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "db8803bc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:39.391339Z",
     "iopub.status.busy": "2026-11-09T12:45:39.391078Z",
     "iopub.status.idle": "2026-11-09T12:45:39.408691Z",
     "shell.execute_reply": "2026-11-09T12:45:39.408260Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime</th>\n",
       "      <th>value</th>\n",
       "      <th>feature_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-01-01 00:00:00+00:00</td>\n",
       "      <td>113.001142</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-02-01 00:00:00+00:00</td>\n",
       "      <td>164.920763</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-03-01 00:00:00+00:00</td>\n",
       "      <td>99.281869</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-04-01 00:00:00+00:00</td>\n",
       "      <td>373.389638</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-05-01 00:00:00+00:00</td>\n",
       "      <td>155.809683</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   datetime       value  feature_id\n",
       "0 2016-01-01 00:00:00+00:00  113.001142           1\n",
       "1 2016-02-01 00:00:00+00:00  164.920763           1\n",
       "2 2016-03-01 00:00:00+00:00   99.281869           1\n",
       "3 2016-04-01 00:00:00+00:00  373.389638           1\n",
       "4 2016-05-01 00:00:00+00:00  155.809683           1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "areal = pd.DataFrame({\"datetime\": dates, \"value\": rng.gamma(shape=6, scale=20, size=len(dates)), \"feature_name\": \"Catchment\"})\n",
    "fdb.insert_records(dataframe=areal, spec_name=\"PPT_MONTHLY_AREAL\", flag=1)\n",
    "fdb.query_records(query_dict={\"feature_name\": \"Catchment\"})[[\"datetime\", \"value\", \"feature_id\"]].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44ac9654",
   "metadata": {},
   "source": [
    "All the databases of this tutorial are GeoPackage files, so they open directly in QGIS, with the layer and every table of the database:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "d1ae4641",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2026-11-09T12:45:39.410257Z",
     "iopub.status.busy": "2026-11-09T12:45:39.410088Z",
     "iopub.status.idle": "2026-11-09T12:45:39.421278Z",
     "shell.execute_reply": "2026-11-09T12:45:39.420750Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>name</th>\n",
       "      <th>geometry_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>features</td>\n",
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       "      <th>3</th>\n",
       "      <td>sources</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>statistics</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>specs</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>records</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         name geometry_type\n",
       "0    features       Unknown\n",
       "1   variables           NaN\n",
       "2       flags           NaN\n",
       "3     sources           NaN\n",
       "4  statistics           NaN\n",
       "5       specs           NaN\n",
       "6     records           NaN"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fdb.close()\n",
    "gpd.list_layers(OUTPUT_DIR / \"features_db.gpkg\")"
   ]
  }
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